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Time-restricted feeding (TRF) is an effective means that can efficiently regulate the metabolism and health of animals and humans. However, the effect of TRF on hypothalamic function remains unclear.Results showed that TRF significantly increased the activities of digestive enzymes lipase, maltase in the duodenum and lipase, trypsin in the pancreas whereas significantly decreased serum gastrointestinal hormones gastrin, glucagon-like peptide-1, cholecystokinin, peptide YY, and ghrelin. Metabolites related to amino acid metabolism, including citrulline, kynurenine, N-acetylleucine, l-tryptophan, and l-tyrosine, significantly increased in the TRF group. Differential metabolites were mainly enriched in phenylalanine, tyrosine, and tryptophan biosynthesis and tryptophan metabolism. Transcriptomic analysis of hypothalamus showed that a total of 462 differentially expressed genes (DEGs) were significantly changed by TRF. In particular, DEGs such as DDC, TH, GOT2, and DBH involved in aromatic amino acid metabolism pathways were significantly downregulated, whereas the expression of CYP1B1 was significantly upregulated. Moreover, DEGs (PDYN and PPP3CA) involved in amphetamine addiction and cocaine addiction were also downregulated in the TRF group.Taken together, these results suggested that TRF improved the digestion and absorption of nutrients and thus increased the accessibilities of aromatic amino acids. The increasing of circulating aromatic amino acids might mediate the regulatory neuroendocrine effects of TRF regimes on the hypothalamus functions, especially on drug addictions. This study reveals a possible mechanism underlying the effects of regulating feeding patterns on the function of the hypothalamus by altering aromatic amino acids metabolism. © 2022 Society of Chemical Industry.

Two new inclusion compounds, (CH3)3N+CH2 COO−·2(NH2)2CS (1) and (CH3)3N+ CH2COO−·2(NH2)2CO (2), have been prepared and characterized by X-ray crystallography. In the crystal structure of 1, the cat...

Hexavalent chromium (Cr

The spatial correlation among different tissue components is an essential characteristic for diagnosis of breast cancers based on histopathological images. Graph convolutional network (GCN) can effectively capture this spatial feature representation, and has been successfully applied to the histopathological image based computer-aided diagnosis (CAD). However, the current GCN-based approaches need complicated image preprocessing for graph construction. In this work, we propose a novel CAD framework for classification of breast histopathological images, which integrates both convolutional neural network (CNN) and GCN (named CNN-GCN) into a unified framework, where CNN learns high-level features from histopathological images for further adaptive graph construction, and the generated graph is then fed to GCN to learn the spatial features of histopathological images for the classification task. In particular, a novel clique GCN (cGCN) is proposed to learn more effective graph representation, which can arrange both forward and backward connections between any two graph convolution layers. Moreover, a new group graph convolution is further developed to replace the classical graph convolution of each layer in cGCN, so as to reduce redundant information and implicitly select superior fused feature representation. The proposed clique group GCN (cgGCN) is then embedded in the CNN-GCN framework (named CNN-cgGCN) to promote the learned spatial representation for diagnosis of breast cancers. The experimental results on two public breast histopathological image datasets indicate the effectiveness of the proposed CNN-cgGCN with superior performance to all the compared algorithms.

Modifying nanocrystals with functional materials have been common strategy to enlarge the enhancing ability on oral absorption via nanocrystals; however, whether the functional materials have played their full enhancing ability in oral absorption is still unknown. In this study, we synthetized a novel chitosan-based copolymer (the copolymer of sodium dodecyl sulfate (SDS), chitosan (CS) and D-α-Tocopherol polyethylene glycol 1000 succinate, SDS-CS-TPGS), and modified nanocrystals with this copolymer, aiming to enhance the oral absorption of polymer andrographolide (ADR). In real-time distribution study, we found the distribution of ADR, SDS, CS and TPGS varies in gastrointestinal tract, while the distribution of ADR and SDS-CS-TPGS was similar, revealing the SDS-CS-TPGS could able to participate in the absorption process of andrographolide timely. To explore the oral absorption enhancing ability of SDS-CS-TPGS, we prepared a series of nanocrystals modified with different materials and explored their pharmacokinetic performances on SD rats. The results showed the nanocrystals modified with SDS-CS-TPGS (S-C-TANs) exhibited the highest bioavailability, which could enhance the AUC0-∞ of ADR from 1.291 mg/L*h to 5.275 mg/L*h (enhanced for about 4.09-folds). The enhanced anti- inflammatory efficacy was also found on ICR mice by employing ear swelling rate, TNF-α, IL-1β and IL-6 and pharmacodynamic index. These results indicated that modified with synthesized copolymer containing different functional stabilizers is an efficient strategy to enlarge the enhancing ability on oral absorption of nanocrystals.

Convolutional neural networks (CNNs) and their variants have been successfully applied to the electroencephalogram (EEG) based motor imagery (MI) decoding task. However, these CNN-based algorithms generally have limitations in perceiving global temporal dependencies of EEG signals. Besides, they also ignore the diverse contributions of different EEG channels to the classification task. To address such issues, a novel channel attention based MLP-Mixer network (CAMLP-Net) is proposed for EEG-based MI decoding. Specifically, the MLP-based architecture is applied in this network to capture the temporal and spatial information. The attention mechanism is further embedded into MLP-Mixer to adaptively exploit the importance of different EEG channels. Therefore, the proposed CAMLP-Net can effectively learn more global temporal and spatial information. The experimental results on the newly built MI-2 dataset indicate that our proposed CAMLP-Net achieves superior classification performance over all the compared algorithms.

The thick-slice magnetic resonance (MR) images are often structurally blurred in coronal and sagittal views, which causes harm to diagnosis and image post-processing. Deep learning (DL) has shown great potential to reconstruct the high-resolution (HR) thin-slice MR images from those low-resolution (LR) cases, which we refer to as the slice interpolation task in this work. However, since it is generally difficult to sample abundant paired LR-HR MR images, the classical fully supervised DL-based models cannot be effectively trained to get robust performance. To this end, we propose a novel Two-stage Self-supervised Cycle-consistency Network (TSCNet) for MR slice interpolation, in which a two-stage self-supervised learning (SSL) strategy is developed for unsupervised DL network training. The paired LR-HR images are synthesized along the sagittal and coronal directions of input LR images for network pretraining in the first-stage SSL, and then a cyclic interpolation procedure based on triplet axial slices is designed in the second-stage SSL for further refinement. More training samples with rich contexts along all directions are exploited as guidance to guarantee the improved interpolation performance. Moreover, a new cycle-consistency constraint is proposed to supervise this cyclic procedure, which encourages the network to reconstruct more realistic HR images. The experimental results on a real MRI dataset indicate that TSCNet achieves superior performance over the conventional and other SSL-based algorithms, and obtains competitive qualitative and quantitative results compared with the fully supervised algorithm.

Quantitative Susceptibility Mapping (QSM) is a new phase-based technique for quantifying magnetic susceptibility. The existing QSM reconstruction methods generally require complicated pre-processing on high-quality phase data. In this work, we propose to explore a new value of the high-pass filtered phase data generated in susceptibility weighted imaging (SWI), and develop an end-to-end Cross-connected $\Psi -$Net $(\mathrm{C}\Psi -$Net) to reconstruct QSM directly from these phase data in SWI without additional pre-processing. $\mathrm{C}\Psi -$Net adds an intermediate branch in the classical U-Net to form a $\Psi -$like structure. The specially designed dilated interaction block is embedded in each level of this branch to enlarge the receptive fields for capturing more susceptibility information from a wider spatial range of phase images. Moreover, the crossed connections are utilized between branches to implement a multi-resolution feature fusion scheme, which helps $\mathrm{C}\Psi -$Net capture rich contextual information for accurate reconstruction. The experimental results on a human dataset show that $\mathrm{C}\Psi -$Net achieves superior performance in our task over other QSM reconstruction algorithms.

Dissolved oxygen (DO) played a short board effect on nitrogen biotransformation and pollutant metabolism. This study for the first time explored the key role of different levels of DO (covering anaerobic, anoxic and aerobic) on hydrolyzed polyacrylamide (HPAM) bioconversion. HPAM was metabolized to intermediates with different chain length. Volatile fatty acid (VFA) production rose first and then descended with DO concentration (0–2 mg·L−1), and the maximum reached 92.5 mg·L−1 when DO was 0.5 mg·L−1. Total nitrogen (TN) removal increased first and then dropped with DO concentration, and the maximum (61.4%) occurred at 0.5 mg·L−1 DO. NH4+-N dipped from 42.8 to 0 mg·L−1 and NO3–-N rose from 0 to 32.8 mg·L−1 with DO concentration. The changes of enzyme activities were consistent with those of VFA production and TN removal, which were related to HPAM metabolism and N bioconversion. Microbial function was correlated to HPAM metabolism, N bioconversion and key enzyme.

The interaction between asphaltenes and hydrolyzed polyacrylamide (HPAM) at the oil-water interface is the key factor leading to the stability of polymer-containing wastewater/oil sludge in the oilfield. In this study, the asphaltenes extracted from polymer-containing oil sludge were systematically characterized and the interaction between asphaltenes and HPAM at the toluene/water interface was studied. We found that asphaltenes had a moderately aromatic structure and a large number of fatty chains. There was a strong π-π stacking interaction among asphaltenes molecules. Asphaltenes and HPAM could reduce the interfacial tension of oil/water, increase the interfacial dilatational modulus, and enhance the stability of emulsion synergistically. Both the asphaltenes concentration and the HPAM concentration affected the interfacial stability. With the changes of the two components concentrations, there was a synergistic or competitive effect between asphaltenes and HPAM at the toluene/water interface. At low concentrations of asphaltenes (Casphaltenes = 100 ppm) the addition of HPAM (CHPAM = 300 ppm) reduced the interfacial tension by 0.52 ± 0.17 mN/m, improved the dilatational modulus by 7.75 ± 0.4 mN/m (the growth rate was 57.33%), asphaltenes molecules and HPAM molecules co-adsorbed on toluene/water interface. For the high concentration of asphaltenes (Casphaltenes = 500 ppm), the addition of HPAM (CHPAM = 300 ppm) rarely reduced the interfacial tension, a few increased the dilatational modulus by 0.69 ± 0.15 mN/m, asphaltenes molecules competed with HPAM molecules to adsorb on toluene/water interface. The presence of HPAM in the aqueous phase changed the emulsion from water-in-oil type (W/O) to oil-in-water type (O/W). The increase of asphaltenes and HPAM concentration is beneficial to the increase of emulsion interfacial film strength (the addition of HPAM increased the yield stress of emulsion interfacial film by 22 times) and the enhancement of emulsion storage stability of 100% maximum. These results provided a theoretical foundation for treating polymer-containing oil sludge in oil fields.

Polymer-containing oil sludge inevitably was produced during oilfield exploitation, which caused serious environmental pollution and resource waste. In this study, thermochemical cleaning method was firstly applied to treat oil sludge from Shengli Oilfield to solve environmental pollution, recover crude oil and achieve resource utilization. Through preliminary screening of cleaning agents, such as hexadecyl trimethyl ammonium bromide (CTMAB), sodium dodecyl benzene sulfonate (SDBS), fatty alcohol polyoxyethylene sulfate (AES), rhamnolipid, fatty alcohol ethoxylate (AEO-9), polyoxyethylene castor oil ether (EL100), polyoxypropylene polyoxyethylene propylene glycol ether (SP169), Tween 80, and Na2SiO3, three cleaning agents including Na2SiO3, SP169 and rhamnolipid were more effective than others. The optimized conditions of three cleaning agents were determined by measuring oil recovery rate. Afterwards, the ratio of Na2SiO3, SP169, and rhamnolipid in the mixed cleaning agent was 10:1:0.5. Under the cleaning conditions with an agent content of 1.1 %, a liquid-solid ratio (L/S) of 7:1, a temperature of 70 °C, a stirring speed of 400 rpm, a cleaning time of 60 min, the highest oil recovery rate could reach 98 %. Compared with a single cleaning agent, the average oil recovery rate was increased by 20 %. The mechanism of thermochemical cleaning treatment of oil sludge was further discussed. This research provided a theoretical foundation and technical support for the efficient treatment and resource recovery of polymer-containing oil sludge in oilfields.

Background and aim Severe burn is known to induce a series of pathological responses resulting in increased susceptibility to systemic inflammatory response and multiple organ failure, but the underlying molecular mechanism remains unclear at present. The main aim of this study was to expand our understanding of the events leading to circulating leukocyte response after burn by subjecting the gene expression profiles to a bioinformatic analysis. Materials and methods Comprehensive gene expression analysis was performed to identify differentially expressed genes (DEGs) using the expression profile GSE7404 (Mus musculus, circulating leukocyte, 25% of total body surface area (TBSA), full thickness) downloaded from the Gene Expression Omnibus, followed by the Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. In addition, a postburn protein–protein interaction (PPI) network was constructed to identify potential biomarkers. Results Maximum changes in the gene expression profile were detected 1 day post burn. Separate Gene Ontology (GO) functional enrichment analysis for upregulated and downregulated DEGs revealed significant alterations of genes related to biological process such as “response to stimuli,” “metabolic,” “cellular and immune system processes,” “biological regulation,” and “death” in the leukocyte transcriptome after the burn. The KEGG pathway enrichment analysis showed that the upregulated DEGs were significantly enriched in the nodes of immunorelated and signal transduction-related pathways, and the downregulated genes were significantly enriched for the immunorelated pathways. The PPI network and module analysis revealed that, 1 day after the burn, lymphocyte-specific protein tyrosine kinase (Lck) (downregulated), Jun (upregulated), Cd19 (downregulated), Stat1 (downregulated), and Cdk1 (upregulated) were located centrally in both the PPI network and modules. Conclusions Based on an integrated bioinformatic analysis, we concluded that Lck, Jun, Cd19, Stat1, and Cdk1 may be critical 1 day after the burn. These findings expand our understanding of the molecular mechanisms of this important pathological process. Further studies are needed to support our work, focused on identifying candidate biomarkers with sufficient predictive power to act as prognostic and therapeutic biomarkers for burn injury.

To screen the genes related with leukocyte responses in mice early after burn injury by bioinformatic analysis of the gene expression profiling data.Gene expression profiles were obtained from GEO (GSE7404, Mouse musculus, 25% TBSA, full-thickness) database. After screening of the differentially expressed genes (DEGs) through paired-sample t-test and fold-change, DAVID online tools were used to select the DEGs related to leukocyte responses to burns by GO functional enrichment analysis; the interacting genes identified through KEGG pathway enrichment analysis were transferred to STRING to construct the protein-protein interaction (PPI) network. Biological annotation of the sub-networks was executed using the software Cytoscape. Real-time PCR was used to verify the DEGs identified in mice.Of the 259 leukocyte response-related DEGs screened at 1 day post-burn, 118 were up-regulated and 141 were down-regulated. KEGG pathway enrichment analysis showed that the pathways were associated with the immune function, cell growth and cell death. PPI network and module analysis suggested that some of genes (such as Lck, Stat1, Myd88, Stat3, and Jun) play critical roles in the PPI network post-burn. RT-PCR results were consistent with those of bioinformatic analysis.Lck, Stat1, Myd88, Stat3, and Jun might be critical players in the development of leukocyte response in mice early after burn injury. Our finding provides new insights into the pathogenesis of leukocyte response to burn injury and identifies several potential biomarkers for burn treatment.

To investigate the effect of antibacterial peptide LL-37 on the integrity of Acinetobacter baumannii biofilm.A model of Acinetobacter baumannii biofilm in vitro was constructed by plates and crystal violet staining method, and the minimal inhibitory concentration of LL-37 was determined by broth dilution. The biofilm morphology was observed by scanning electron microscopy and biofilm formation was analyzed by the crystal violet staining of the adherent biofilm in the presence of antibacterial peptide LL-37.In the Acinetobacter baumannii biofilm model, the minimal inhibitory concentration of LL-37 was 64 µg/ml; LL-37 caused structural damage of the biofilm at a low concentration of 2.5 µg/ml. The biofilm was decreased gradually as the concentration of LL-37 increased.LL-37 even at a concentration far below its minimal inhibitory concentration can cause structural damage of Acinetobacter baumannii biofilm in vitro.